Throttle-triggered suggestions

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing orders. The methods include actions of receiving an order for an item to be fulfilled by the fulfillment center for a fulfillment time, obtaining a portion of stored historical preparation information that is relevant to fulfillment of the item by the fulfillment time, obtaining current order information that describes other orders to be fulfilled at the fulfillment center, determining to provide an alternate suggestion, identifying at least one of an alternate item, an alternate fulfillment center, or an alternate time to include in the alternate suggestion, generating the alternate suggestion based on the identification of at least one of the alternate item, the alternate fulfillment center, or the alternate time to include in the alternate suggestion, and providing the alternate suggestion to the user device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/148,450, filed Apr. 16, 2015, which is herebyincorporated by reference in its entirety.

TECHNICAL FIELD

This disclosure generally relates to a system architecture.

BACKGROUND

System architectures may be used to manage orders. In some instances,system architectures may manage orders placed through the Internet.

SUMMARY

In general, an aspect of the subject matter described in thisspecification may involve a process for managing orders. The operationsinclude tracking, over time and at a server, performance of afulfillment center in fulfilling orders for items at the fulfillmentcenter and user responses to alternate suggestions and based on thetracking, storing, in an electronic storage accessible to the server,historical preparation information that describes historical performanceof the fulfillment center in fulfilling orders for items at thefulfillment center. Additional actions include based on the tracking,storing, in the electronic storage accessible to the server, historicaluser information that indicates a likelihood that a user will accept analternate suggestion and receiving, at the server and from a user deviceassociated with the user, an order for an item to be fulfilled by thefulfillment center for a fulfillment time, where the order indicates theitem and the fulfillment center fulfills the order by enabling anemployee at the fulfillment center to prepare the item for the user.Further actions include obtaining a portion of the stored historicalpreparation information for the fulfillment center that describeshistorical performance of the fulfillment center that is relevant tofulfillment of the item by the fulfillment time and obtaining currentorder information that describes other orders to be fulfilled at thefulfillment center. Additional actions include determining, to providean alternate suggestion based at least on a portion of the storedhistorical preparation information and the current order information, inresponse to determining to provide the alternate suggestion, identifyingat least one of an alternate item, an alternate fulfillment center, oran alternate time to include in the alternate suggestion based on thestored historical user information that indicates a likelihood that theuser will accept the alternate suggestion, generating the alternatesuggestion based on the identification of at least one of the alternateitem, the alternate fulfillment center, or the alternate time to includein the alternate suggestion, and providing the alternate suggestion tothe user device.

In another aspect, a method may include one or more of the operationsdescribed above. In yet another aspect, a computer-readable storagemedium may be operable to cause a processor to perform one or more ofthe operations described above.

Implementations may include one or more of the following features. Forexample, determining, to provide an alternate suggestion based at leaston a portion of the stored historical preparation information and thecurrent order information includes determining that the order cannot befulfilled by the fulfillment center for the fulfillment time.

In certain aspects, determining, to provide an alternate suggestionbased at least on a portion of the stored historical preparationinformation and the current order information includes determining thatthe order can be fulfilled by the fulfillment center for the fulfillmenttime but another order is likely to be placed for the item for thefulfillment time.

In some aspects, determining, to provide an alternate suggestion basedat least on a portion of the stored historical preparation informationand the current order information includes determining that thealternate suggestion is likely to result in a better business value foran entity providing the server.

In some implementations, identifying at least one of an alternate item,an alternate fulfillment center, or an alternate time to include in thealternate suggestion based on the stored historical user informationthat indicates a likelihood that the user will accept the alternatesuggestion includes determining that the alternate suggestion can befulfilled.

In certain aspects, identifying at least one of an alternate item, analternate fulfillment center, or an alternate time to include in thealternate suggestion based on the stored historical user informationthat indicates a likelihood that the user will accept the alternatesuggestion includes determining that the user is likely to be interestedin the alternate item based on the stored historical user informationthat indicates a likelihood that the user will accept the alternatesuggestion and in response to determining that the user is likely to beinterested in the alternate item based on the stored historical userinformation that indicates a likelihood that the user will accept thealternate suggestion, identifying the alternate item.

In some aspects, identifying at least one of an alternate item, analternate fulfillment center, or an alternate time to include in thealternate suggestion based on the stored historical user informationthat indicates a likelihood that the user will accept the alternatesuggestion includes determining a likely route for the user based atleast on the stored historical user information that indicates alikelihood that the user will accept the alternate suggestion,determining that the alternate fulfillment center is accessible alongthe likely route, and in response to determining that the alternatefulfillment center is accessible along the likely route, identifying thealternate fulfillment center.

In some implementations, identifying at least one of an alternate item,an alternate fulfillment center, or an alternate time to include in thealternate suggestion based on the stored historical user informationthat indicates a likelihood that the user will accept the alternatesuggestion includes determining an earliest time after the fulfillmenttime that the order can be fulfilled, determining whether the earliesttime satisfies a time threshold with the fulfillment time, and inresponse to determining that the earliest time satisfies the timethreshold with the fulfillment time, identifying the earliest time asthe alternate time.

In certain aspects, generating the alternate suggestion based on theidentification of at least one of the alternate item, the alternatefulfillment center, or the alternate time to include in the alternatesuggestion includes determining a discount to include in the alternatesuggestion and including the discount in the alternate suggestion.

In some aspects, additional actions include reserving an order for thealternate suggestion for a predetermined time before receiving aresponse from the user device to the alternate suggestion.

In some implementations, generating the alternate suggestion based onthe identification of at least one of the alternate item, the alternatefulfillment center, or the alternate time to include in the alternatesuggestion includes generating, for each of multiple candidate alternatesuggestions, outcomes scores that each indicate an estimated value ofthe candidate alternate suggestion and selecting a predetermined numberof the candidate alternate suggestions based at least on the outcomescores.

In certain aspects, identifying at least one of an alternate item, analternate fulfillment center, or an alternate time to include in thealternate suggestion based on the stored historical user informationthat indicates a likelihood that the user will accept the alternatesuggestion is further based at least on historical performance of thealternate fulfillment center, current orders for the alternatefulfillment center, historical performance for the alternate item,current orders for the alternate items, or current orders for thealternate time.

The details of one or more implementations of the subject matterdescribed in this specification are set forth in the accompanyingdrawings and the description below. Other potential features, aspects,and advantages of the subject matter will become apparent from thedescription, the drawings, and the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of an example system for managing orders.

FIGS. 2A and 2B illustrate example interfaces used in managing orders.

FIGS. 3 and 4 are interaction diagrams of managing orders.

FIGS. 5 and 6 are flowcharts of example processes for managing orders.

FIG. 7 is a block diagram of an example system for real time delivery.

FIG. 8 illustrates a schematic diagram of an exemplary generic computersystem.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

A system may be used to manage orders for items. For example, a systemmay enable a user to use an application on a mobile device to ordercoffee from a coffee shop to be ready for pick up when the user arrivesat the coffee shop. A server may receive the order from the mobiledevice and determine whether the coffee shop can fulfill the order. Inresponse to determining that the coffee shop cannot fulfill the order,the server may provide the mobile device an indication that the coffeeshop cannot fulfill the order, and may also provide an alternatesuggestion to the user. By determining whether the orders may befulfilled by particular times and providing alternate suggestions, thesystem may enable users to avoid waiting for orders that would not beable to be fulfilled by particular fulfillment times and enable users toplace alternate orders.

FIG. 1 is a block diagram of an example system 100. Briefly, and asdescribed in further detail below, the system 100 may include a userdevice 110, a server 120, a historical information database 122, asuggestion rule database 124, a current order database 126, andfulfillment center workstations 130, 140.

The user device 110 may be a computing device used by a user 112. Forexample, the user device 110 may be a smart phone, a tablet computer, alaptop computer, a desktop computer, or another type of computingdevice. The user device 110 may include a display that provides aninterface for the user 112 to place an order with the system 100 andthat provides information to the user 112 regarding the order. Forexample, the user device 110 may display an interface through which theuser 112 may request to provide an order for coffee from fulfillmentcenter A at 9 AM, and in response to which, the user device 110 maydisplay a notification that “Sorry, coffee is not available at 9 AM fromfulfillment center A. Would you like to order coffee instead for 9 AMfrom fulfillment center B a block away?”

The user device 110 may determine to provide an order for a particularitem to the server 120. For example, the user device may determine toprovide an order for a cup of coffee. The user device 110 may determineto provide an order for a particular item to the server 120 in responseto input from the user 112. For example, the user device 110 may displaya graphical user interface through which the user 112 may select an itemto order, a fulfillment center from which to pick up the order, and atime to pick up the order, and interact with a submission element toindicate to the user device 110 to provide the order to the server 120.In another example, the user device 110 may audibly output prompts forthe user 112 to use speech to select an item to order, a fulfillmentcenter from which to pick up the order, and a time to pick up the order,and say “submit order” to indicate to the user device 110 to provide theorder to the server 120.

The user device 110 may provide, to the server 120, an order thatindicates one or more particular items. For example, an order mayindicate that one cup of coffee is ordered. In another example, an ordermay indicate that two cups of tea and a bagel are ordered. In someimplementations, the order may indicate a fulfillment center. Forexample, the order may indicate fulfillment center A and a currentlocation of the user, and the server 120 may determine a fulfillmenttime corresponding to when the user 112 is estimated to arrive atfulfillment center A based on the current location of the user.

In some implementations, the order may indicate a fulfillment time. Forexample, the order may indicate a fulfillment time and a currentlocation of the user, and the server 120 may determine that the user 112may reach fulfillment center A along the user's expected route by thefulfillment time based on the current location of the user. In someimplementations, the order may indicate neither a fulfillment center nora fulfillment time. For example, the order may indicate that the user112 would like coffee when the user 112 arrives at the user's office anda current location of the user 112, and the server 120 may determine afulfillment time based on when the user 112 is estimated to arrive atthe office based on the current location of the user 112 and determine afulfillment center based on a distance of the fulfillment center fromthe office of the user 112.

In response to providing an order, the user device 110 may receive aresponse from the server 120 to the order. In some implementations, theuser device 110 may receive an indication that the order can befulfilled and was successfully placed. For example, the user device 110may receive an indication confirming that the order was placed and inresponse display, “Order placed.” In some implementations, the userdevice 110 may receive an indication that the order cannot be fulfilled.For example, the user device 110 may receive an indication that theorder cannot be fulfilled and in response display, “Sorry, that ordercannot be fulfilled.”

In some implementations, the user device 110 may receive an alternatesuggestion. For example, the user device 110 may receive an indicationthat the order cannot be fulfilled and receive an alternate suggestionand display, “Sorry, coffee is not available at 9 AM from fulfillmentcenter A. Would you like to order coffee instead for 9 AM fromfulfillment center B a block away?” In another example, the user device110 may receive an indication that the order can be fulfilled andreceive an alternate suggestion and display, “Coffee is available at 9AM from fulfillment center A, but a special offer is also available forcoffee at 9:10 AM for a 5% discount. Would you like to order coffeeinstead for 9:10 AM?”

The user device 110 may receive a response from the user 112 to analternate suggestion and provide the response to the server 120. Forexample, the user device 110 may receive user input accepting analternate suggestion to order coffee from another fulfillment center,and provide a response to the server 120 that the user 112 has selectedthe alternate suggestion to order coffee from another fulfillmentcenter.

In some implementations, the user device 110 may receive multiplealternate suggestions, provide multiple options to the user 112corresponding to the multiple alternate suggestions, receive a selectionof an alternate suggestion, and provide an indication of the selectionof the alternate suggestion to the server 120. For example, the userdevice 110 may receive an alternate suggestion of an alternate item andanother alternate suggestion of an alternate fulfillment center, providetwo options corresponding to the two alternate suggestions, receive auser selection of the option for the alternate fulfillment center, andprovide an indication to the server 120 that the user 112 accepted thealternate suggestion of the alternate fulfillment center.

The server 120 may be a computing device that manages orders for items.The server 120 may be in communication with the user device 110, thehistorical information database 122, the suggestion rules database 124,the current order database 126, and the fulfillment center workstations130, 140 over a network. For example, the server 120 may use theInternet to communicate with the user device 110 and the fulfillmentcenter workstations 130, 140. In another example, the server 120 may usea local network to communicate with the historical information database122, the suggestion rule database 124, and the current order database126.

The server 120 may track performance of fulfillment centers infulfilling orders over time, and store historical preparationinformation in the historical information database 122 that describesthe historical performance. For example, the server 120 may receiveperformance information from the fulfillment center over a span of amonth, where the performance information indicates whenever a cup ofcoffee is produced, and store historical preparation information thatindicates that on average, the particular fulfillment center is able toproduce ten cups of coffee every ten minutes. In another example, theserver 120 may receive performance information from the fulfillmentcenter for a one week period that indicates whenever a cup of coffee isproduced, and store historical preparation information that indicatesthat in the morning the particular fulfillment center is able to producetwelves cups of tea every ten minutes and in the afternoon theparticular fulfillment center is able to produce eight cups of tea everyten minutes. In yet another example, the server 120 may receiveperformance information from the fulfillment center for a half yearperiod that indicates whenever a cup of coffee is produced and whatemployees are working during that time, and store historical preparationinformation that indicates that whenever employee A is producing coffee,employee A is able to produce fifteen cups of coffee every ten minutes,and whenever employee B is producing coffee, employee B is able toproduce five cups of coffee every ten minutes.

The server 120 may track user orders and responses to alternatesuggestions over time and store, in the historical information database122, historical user information that indicates a likelihood that a userwill accept an alternate suggestion. For example, the server 120 maytrack the types of items that that the user 112 orders and storeinformation that indicates alternate items that the user 112 may belikely to desire. In another example, the server 120 may track thefulfillment centers at which the user 112 places orders and storeinformation that indicates alternate fulfillment centers that the user112 may accept for providing an item. In yet another example, the server120 may track the alternate suggestions that the user 112 accepts andrejects, and store information that indicates the types of alternatesuggestions that the user 112 has accepted or rejected, and amounts ofdiscounts at which the user 112 has accepted or rejected alternatesuggestions for different types of alternate suggestions.

The server 120 may track current orders and store current orderinformation in the current order database 126. The current orderinformation may indicate current orders that are to be fulfilled byfulfillment centers. The current order information may indicateparticular orders, where for each order, the current order informationindicates one or more particular items, a quantity for each particularitem, a fulfillment center to fulfill the order, and a fulfillment timefor fulfilling the order.

The server 120 may receive an order from the user device 110 for an itemto be fulfilled by the fulfillment center for a fulfillment time, wherethe order indicates the item and the fulfillment center fulfills theorder by enabling an employee at the fulfillment center to prepare theitem for the user 112. As described above, the order the server 120receives from the user device 110 may indicate one or more of afulfillment center or a fulfillment time, or the server 120 maydetermine one or more of a fulfillment center or a fulfillment timebased on a current location of the user 112 indicated by the order.

In response to receiving an order from the user device 110, the server120 may determine whether the fulfillment center can fulfill the orderby the fulfillment time. For example, in response to receiving an orderfor coffee at 9 AM by fulfillment center A, the server 120 may determinethat fulfillment center A cannot fulfill the order. In another example,in response to receiving an order for coffee at 9 AM by fulfillmentcenter A, the server 120 may determine that fulfillment center A canfulfill the order.

The server 120 may determine whether the fulfillment center can fulfillthe order by the fulfillment time based at least on current orderinformation and a portion of the stored historical preparationinformation for the fulfillment center. For example, the server 120 maydetermine that an order for coffee at 9 AM by fulfillment center A canbe fulfilled based on current order information that indicates that onlynine cups of coffee have been ordered for 9 AM and a portion of storedhistorical preparation information for fulfilment center A thatindicates that fulfillment center A can produce ten cups of coffee at 9AM. In another example, the server 120 may determine that an order forcoffee at 9 AM by fulfillment center A cannot be fulfilled based oncurrent order information that indicates that ten cups of coffee havebeen ordered for 9 AM and a portion of stored historical preparationinformation for fulfilment center A that indicates that fulfillmentcenter A can produce ten cups of coffee at 9 AM. In yet another example,the server 120 may determine that an order for coffee at 9 AM forfulfillment center A cannot be fulfilled based on current orderinformation that indicates that seven cups of coffee and two cups ofespresso have been ordered for 9 AM and a portion of stored historicalpreparation information for fulfilment center A that indicates thatfulfillment center A can only produce a combined total of nine cups forcoffee and espresso at 9 AM.

The server 120 may determine whether the fulfillment center can fulfillthe order by the fulfillment time based at least on determining that ademand for the item indicated by the current order information is notless than a production rate estimate for the item. For example, theserver 120 may determine that the fulfillment center cannot fulfill theorder based on determining that a demand for coffee indicated by thecurrent order information is ten cups, which is not less than aproduction rate estimate of ten cups for coffee. In another example, theserver 120 may determine that the fulfillment center can fulfill theorder based on determining that a demand for coffee indicated by thecurrent order information is eight cups, which is less than a productionrate estimate of nine cups for coffee.

The server 120 may determine the demand for the item based ondetermining a quantity of the item ordered from the fulfillment centerfor a particular time. For example, the server 120 may determine thatthe current order information indicates that eight cups of coffee havebeen ordered for 9 AM at fulfillment center A, and in response,determine that the demand for coffee for 9 AM at fulfillment center A iseight cups. In another example, the server 120 may determine that thecurrent order information indicates that ten cups of coffee have beenordered for 9 AM at fulfillment center A, and in response, determinethat the demand for coffee for 9 AM at fulfillment center A is ten cups.

The server 120 may determine a production rate estimate for an itembased on a portion of the stored historical preparation information forthe fulfillment center. For example, the server 120 may determine fromthe stored historical preparation information for fulfillment center Athat fulfillment center A typically produces ten cups of coffee, and inresponse, determine that the production rate estimate for coffee forfulfillment center A is ten cups per every ten minutes. In anotherexample, the server 120 may determine from the stored historicalpreparation information for fulfillment center A that fulfillment centerA typically produces eight cups of coffee at 9 AM, and in response,determine that the production rate estimate for coffee for fulfillmentcenter A is eight cups at 9 AM. In yet another example, the server 120may determine from the stored historical preparation information forfulfillment center A that fulfillment center A typically producesfifteen cups of coffee every ten minutes when employee A is working andthat employee A is working at 9 AM, and in response, determine that theproduction rate estimate for coffee when the order is to be fulfilled isfifteen cups every ten minutes.

The server 120 may obtain current order information that describes otherorders to be fulfilled at the fulfillment center based on identifyingorders for items at the fulfillment time whose fulfillment delays thefulfillment of the order for the item. For example, the server 120 mayanalyze stored historical preparation information to determine that foreach cup of tea that a fulfillment center produces, the fulfillment of acup of coffee is delayed. The server 120 may store informationindicating these associations between items whose fulfillment causes adelay in the other and an amount of the delay the items cause on theother items. Accordingly, in response to receiving an order for aparticular item, the server 120 may determine the other types of itemsthat may delay the fulfillment of the particular item based on thestored associations, and obtain current order information that describesorders for the item at the particular time and orders at the particulartime for the other item types that may delay the fulfillment of theitem.

The server 120 may obtain a portion of the stored historical preparationinformation, from the historical information database 122 for thefulfillment center, that describes historical performance of thefulfillment center that is relevant to fulfillment of the item by thefulfillment time. For example, for an order for coffee from fulfillmentcenter A at 9 AM, the server 120 may obtain stored historicalpreparation information from the historical information database 122 forpast orders for coffee from fulfillment center A. In another example,for an order for tea from fulfillment center A at 9 AM, the server 120may obtain stored historical preparation information from the historicalinformation database 122 for past orders for tea at 9 AM fromfulfillment center A. In yet another example, for an order for tea fromfulfillment center A at 9 AM, the server 120 may determine that employeeA is working, and may obtain stored historical preparation informationfrom the historical information database 122 for past orders for teawhen employee A is working at fulfillment center A.

In some implementations, the server 120 may determine whether thefulfillment center can fulfill the order by the fulfillment time basedat least on a contingency amount. For example, the server 120 maydetermine that a fulfillment center can produce ten cups of coffee at 9AM, there are already orders for a total of nine cups of coffee at 9 AM,and there is a contingency amount of one cup of coffee per ten cups ofcoffee, and in response, determine that the order cannot be fulfilled.

A contingency amount may be a buffer for contingencies in fulfillingorders. For example, contingencies may include mistakes in fulfilling anorder. The contingency amount may be preset by an administrator ordynamically determined by the server 120 based on historical preparationinformation stored in the historical information database 122. Forexample, the server 120 may determine that the historical preparationinformation indicates that out of the past week, one out of every tencups of coffee had a mistake, and in response, determine to set acontingency amount of one cup per every ten cups of coffee.

In some implementations, the server 120 may determine whether thefulfillment center can fulfill the order by the fulfillment time basedat least on inventory of the fulfillment center. For example, the server120 may determine that a fulfillment center's inventory managementsystem indicates that the fulfillment center has eight hundred bagels tosell that day, determine that orders for six hundred bagels have beenplaced for users, and in response, determine that bagels are stillavailable for sale from the fulfillment center at any desired time thatday. In another example, the server 120 may determine that a fulfillmentcenter's inventory management system indicates that the fulfillmentcenter has eight hundred bagels to sell that day, determine that ordersfor eight hundred bagels have been placed for users, and in response,determine that bagels are not available for sale from the fulfillmentcenter at any desired time that day.

In some implementations, the server 120 may determine whether thefulfillment center can fulfill the order by the fulfillment time basedon scheduled shipments to the fulfillment center. For example, theserver 120 may determine that fulfillment center A has orders forfulfillment before 10 AM for all remaining apples at fulfillment centerA, but that fulfillment center A will have two hundred additional applesarriving at 11 AM, and in response, determine that apples are availablefor sale after 11 AM.

In some implementations, the server 120 may determine whether thefulfillment center can fulfill the order by the fulfillment time basedon an estimated preparation time for handling shipments received by thefulfillment center. For example, the server 120 may determine thatfulfillment center A has orders for fulfillment before 10 AM for allremaining apples at fulfillment center A, but that fulfillment center Awill have two hundred additional apples arriving at 11 AM and that itwill take approximately one hour for the fulfillment center A to handlethe shipment and have the apples ready for sale, and in response,determine that apples are available for sale after 12 PM, one hour afterwhen the shipment arrives.

In some implementations, the server 120 may update current orderinformation stored in the current order database 126 based on monitoringestimated time of arrivals of users. For example, the server 120 maydetermine that an order of coffee is originally scheduled so that thecoffee is ready at 9:00 AM for a user, but the location of the user'sdevice indicates that the user 112 is traveling slower than expected andwill likely not arrive until 9:10. In the example, in response to thedetermination, the server 120 may determine that the coffee shouldinstead be picked up at 9:10 AM and that now only nine cups of coffeehave been scheduled for pick up at 9:00 AM. The server 120 may schedulethe future slot, e.g., 9:10 AM, for the order before releasing theexisting slot, e.g., 9:00 AM. This may ensure that the order always hasa slot in which to be produced and delivered.

The server 120 may determine a location of users based on informationobtained from the user device 110. For example, server 120 may haveaccess to global positioning satellite (GPS) sensor information of theuser device 110 and obtain a latitude and longitude of the user device110. In another example, the server 120 may have access to a location ofthe user device 110 estimated based on available wireless networks oravailable wireless towers.

The server 120 may determine an estimated time of arrival of the userbased on a location of the user device 110 of the user 112. For example,the server 120 may determine that from the current location of the userdevice 110, based on historical information for the user 112 stored inthe historical information database 122, the user 112 typically takestwenty minutes to get to the user's office. In another example, theserver 120 may determine that from the current location of the userdevice 110, the next subway train to the user's office is delayed sothat it arrives in thirty minutes and that based on current calculatedtransit times, the user 112 likely won't get to work until fortyminutes.

The server 120 may calculate an estimated time of arrival of a usergiven a location based on estimated transit times. The server 120 mayestimate transit times based on determining the estimated path the user112 will take to get to the user's destination, and the estimated timeit will take for the user 112 to reach the user's destination using theestimated path. For example, the server 120 may determine that the user112 is traveling by foot, and from the current location the user 112 islikely to take the path to the destination that results in the leastdistance traveled by foot, and calculate the estimated time for someonewalking at the user's pace to travel the distance for the path. Inanother example, the server 120 may determine that the user 112 istraveling by foot, and from the current location to the destination theuser 112 is likely to take the path that the user 112 typically takeswhen traveling by foot from the current location to the user'sdestination, and calculate the estimated time for someone walking at theuser's current walking pace to travel the distance for the path. In yetanother example, the server 120 may determine that the user 112typically takes an amount of time to the destination, and therefore usethat as the estimated time, rather than a typical time for any person.

In response to determining that the fulfillment center cannot fulfillthe order, the server 120 may provide an indication to the user device110 that the order cannot be fulfilled. For example, in response todetermining that the server 120 cannot fulfill an order for coffee at 9AM from fulfillment center A, the server 120 may provide an indicationto the user device 110 that includes “Sorry, coffee is not available at9 AM from fulfillment center A.” In some implementations, the server 120may include an indication of why the order cannot be fulfilled. Forexample, the server 120 may include in the indication “Becausefulfillment center A has run out of cups” or “Because fulfillment centerA cannot produce an additional cup of coffee at 9 AM.”

In some implementations, the server 120 may prevent a user fromrequesting that the user device 110 place an order that cannot befulfilled. For example, before the server 120 receives an order forcoffee at 9 AM from fulfillment center A from the user device 110, theserver 120 may determine that an order for coffee at 9 AM fromfulfillment center A cannot be fulfilled, and provide an indication thatsuch an order cannot be fulfilled to the user device 110 so that theuser device 110 prevents the user 112 from requesting an order forcoffee at 9 AM from fulfillment center A. In a more particular example,in response to the server 120 providing an indication that an ordercannot be fulfilled, the user device 110 may disable and gray out coffeewhen a user selects 9 AM and fulfillment center A, may disable and grayout 9 AM when the user 112 selects coffee and fulfillment center A, ormay disable and gray out fulfillment center A when the user 112 selectscoffee and 9 AM. In another example, the user device 110 may display“Sold Out” or another error message to prevent the user 112 fromrequesting an order that cannot be fulfilled.

In some implementations, in response to determining that an order cannotbe fulfilled, the server 120 may determine to provide an alternatesuggestion. For example, in response to receiving an order for coffee at9 AM from fulfillment center A, the server 120 may provide thenotification, “You're estimated to arrive at fulfillment center A at9:00 AM, but coffee is not available from fulfillment center A until9:10 AM. Would you like to order coffee for 9:10 AM?” In anotherexample, in response to determining that an order cannot be fulfilledbefore the user 112 attempts to place an order, e.g., when a userrequests to view available items for order at 9 AM from fulfillmentcenter A, the server 120 may provide a notification for the user device110 to display next to the item, “This item is sold out by fulfillmentcenter A, but is available from fulfillment center B. Would you like toorder for pickup at fulfillment center B?” In yet another example, inresponse to determining that an order cannot be fulfilled, the server120 may provide a notification to the user device 110 to display “You'reestimated to arrive at fulfillment center A at 9:00 AM but coffee is notavailable until 9:20 AM. Would you like to instead order tea for 9:00AM?”

In some implementations, the server 120 may determine to provide analternate suggestion even if an order can be fulfilled. For example, theserver 120 may determine that providing an alternate suggestion islikely to result in a better business value for an entity fulfilling theorder or managing the server 120. In a particular example, the server120 may determine that the user 112 may agree to an alternate suggestionfor an alternate item with a one-time discount, and that if the user 112continues to order the alternate item in the future instead of theoriginal item, it will result in a greater profit for the entity. Inanother particular example, the server 120 may determine that anotherorder for the same item and for the same fulfillment time is likely tobe received based on historical order information stored in thehistorical information database 122, and determine to provide analternate suggestion based on determining that the particular user ismore likely to accept an alternate suggestion than either a typical useror a particular person likely to place the other order.

In response to determining to provide the alternate suggestion, theserver 120 may identify at least one of an alternate item, an alternatefulfillment center, or an alternate time to include in the alternatesuggestion based on the stored historical user information thatindicates a likelihood that the user 112 will accept the alternatesuggestion. For example, the server 120 may determine to identify tea asan alternate item to coffee based on the stored historical userinformation that indicates a high likelihood that the user 112 willaccept tea instead of coffee, identify fulfillment center B as analternate fulfillment center to fulfillment center A based on the storedhistorical user information that indicates a high likelihood that theuser 112 will accept coffee from fulfillment center B instead offulfillment center A, or identify 9:10 AM as an alternate time to 9:00AM based on the stored historical user information that indicates a highlikelihood that the user 112 will accept the alternate time of 9:10 AMinstead of 9:00 AM.

In providing alternate suggestions, the server 120 may obtain one ormore suggestion rules from the suggestion rule database 124. Forexample, the suggestion rules may specify particular ways of identifyingalternate items, alternate fulfillment centers, or alternate times, asdescribed below.

In identifying an alternate item, the server 120 may determine that theuser 112 is likely to be interested in the alternate item based on thestored historical user information that indicates a likelihood that theuser 112 will accept the alternate suggestion, and in response, identifythe alternate item. For example, in identifying tea instead of coffee,the server 120 may determine a frequency at which the user 112 orderedtea, a frequency at which the user 112 previously ordered tea fromfulfillment center A, or a frequency at which other users, thatpreviously ordered coffee, previously ordered tea.

The server 120 may determine that items more frequently ordered by theuser 112, as indicated by the stored historical user information, aremore likely to be of interest as an alternate item. In another example,in identifying tea instead of coffee, the server 120 may determine afrequency at which the user 112 previously accepted alternatesuggestions for tea instead of coffee or a frequency at which otherusers previously accepted alternate suggestions for tea instead ofcoffee. The server 120 may determine that items more frequently acceptedas an alternate item, as indicated by the stored historical userinformation, are more likely to be of interest as an alternate item. Insome implementations, the server 120 may determine that the user 112 islikely to be interested in the alternate item based on the storedhistorical user information particular to a time of day. For example,the server 120 may determine that the specific user has previouslypurchased an alternative item at a similar time of day, and thereforesuggest that item as an alternate item.

Additionally or alternatively, the server 120 may identify an alternateitem based on determining another item from the fulfillment center issimilar to the item. For example, in response to determining that coffeecannot be provided by fulfillment center A at 9:00 AM, the server 120may determine that fulfillment center A also offers tea, that tea issimilar to coffee, and that tea is available for 9:00 AM, and inresponse, determine to provide a suggestion of ordering tea instead fromfulfillment center A. The server 120 may determine items from afulfillment center are similar based on data explicitly indicatingsimilarity between items. For example, the server 120 may determine teais similar to coffee based on data that explicitly indicates that bothtea and coffee are hot beverages that include caffeine.

In identifying an alternate fulfillment center, the server 120 maydetermine that the user 112 is likely to be interested in the alternatefulfillment center based on the stored historical user information thatindicates a likelihood that the user 112 will accept the alternatesuggestion, and in response, identify the alternate fulfillment center.For example, in identifying fulfillment center B instead of fulfillmentcenter A, the server 120 may determine a frequency at which the user 112orders from fulfillment center B, a frequency at which the user 112previously ordered coffee from fulfillment center B, or the frequency atwhich the user 112 previously accepted alternate suggestions forfulfillment center B instead of fulfillment center A. The server 120 maydetermine that fulfillment centers more frequently ordered from by theuser 112 or more frequently accepted as an alternate fulfillment center,as indicated by the stored historical user information, are more likelyto be of interest as an alternate fulfillment center to the user.

In identifying an alternate fulfillment center, the server 120 maydetermine other fulfillment centers that may fulfill an order for theitem. For example, in response to determining that coffee cannot beprovided by fulfillment center A when the user 112 is scheduled toarrive at fulfillment center A at 9:00 AM, the server 120 may determinethat fulfillment center B is available to provide coffee at 9:00 AM andthat fulfillment center B is only a block from fulfillment center A, andin response, determine to identify fulfillment center B as an alternatefulfillment center.

The server 120 may determine other fulfillment centers that may fulfillan order based on determining an item from a fulfillment center issimilar to an item from an alternate fulfillment centers. For example,the server 120 may determine that coffee from fulfillment center A issimilar to coffee from fulfillment center B. The server 120 maydetermine another fulfillment center offers an item similar to the itemunavailable from the fulfillment center based on data describing theitem. For example, the server 120 may determine that both items aretitled “Coffee.” In another example, the server 120 may access data thatexplicitly indicates that the item coffee from fulfillment center A andfulfilment center B are similar. In yet another example, the server 120may determine that fulfillment center A and fulfillment center B arefrom the same provider, e.g., chain or company, and in response,determine items from fulfillment center A and fulfillment center B withsimilar names are similar items.

In some implementations, the server 120 may determine the alternatefulfillment center based on determining whether the item can be orderedfrom an alternative location which is on the way between the user'scurrent location and their final destination. The alternative locationmay be a different fulfillment center from the same provider, or analternative provider. The server 120 may take into account the locationof access to the alternative location, including altitude of thealternative location in comparison to the user's route.

For example, the server 120 may not identify a fulfillment center abovean underground transport route the user 112 is using, but may identify afulfillment center within a station of the underground transport routeat the beginning or final station, including any stages where the user112 may be swapping modes of transport and therefore travelling withinclose proximity. In another example, the server 120 may determine alikely route for the user 112 based at least on the stored historicaluser information that indicates a likelihood that the user 112 willaccept the alternate suggestion, determine that the alternatefulfillment center is accessible along the likely route, and in responseto determining that the alternate fulfillment center is accessible alongthe likely route, identifying the alternate fulfillment center.

In identifying an alternate time, the server 120 may determine a timethat the order may be fulfilled by the fulfillment center. For example,in response to determining that coffee cannot be provided by fulfillmentcenter A at 9:00 AM, the server 120 may determine that the earliestcoffee is available after 9:00 AM is 9:10 AM. The server 120 maydetermine the time that the order may be fulfilled by the fulfillmentcenter based on determining the next time that the fulfillment centerwill have the item available to provide. For example, the server 120 maydetermine that fulfillment center A may provide up to ten cups of coffeeat 9:10 AM, that only nine cups of coffee have been ordered for 9:10 AM,and in response, determine that the earliest time that the order may befulfilled by the fulfillment center is 9:10 AM.

In some implementations, the server 120 may further determine toidentify an alternate time based on determining the earliest time thatthe order may be fulfilled by a fulfillment center satisfies a timethreshold, and in response, identifying the earliest time as analternate time. For example, the server 120 may determine that theearliest time the coffee is available is 9:10 AM, which is less than athirty minute time threshold than the original fulfillment time of 9:00AM, and in response, identify 9:10 as an alternate time. In anotherexample, the server 120 may determine that the earliest time the coffeeis available is 10:00 AM, which is more than a fifteen minute timethreshold after original fulfillment time of 9:00 AM, and in response,determine not to identify 10:00 AM as an alternate time.

In some implementations, the server 120 may include an incentive inassociation with an alternate suggestion. For example, the server 120may include a discount of 10% for a suggestion for an alternate itemwhen the original order can be fulfilled. In another example, the server120 may include a discount of 5% for a suggestion for an alternatefulfillment center that is a block from the original fulfillment centerwhen the original order cannot be fulfilled.

The server 120 may determine the incentive to include with the alternatesuggestion based on a likelihood that the user 112 will accept thealternate suggestion with the incentive and a value of the order withthe incentive. For example, the server 120 may analyze historical userinformation stored in the historical information database 122 todetermine a likelihood that a particular incentive will cause a user toaccept an alternate suggestion and determine whether the increase in thelikelihood offsets a cost of the incentive. In a particular example, theserver 120 may determine that a 5% discount increases a rate ofacceptance of an alternate time less than fifteen minutes by 50% for aparticular user, and in response, determine to provide the 5% discount.In another particular example, the server 120 may determine that a 10%discount increases a rate of acceptance of an alternate item by 5% for aparticular user, and in response, determine not to provide the 10%discount.

In some implementations, the server 120 may provide multiple alternatesuggestions to the user device 110. For example, in response todetermining that an order of coffee for 9 AM from fulfilment center Acannot be fulfilled, the server 120 may provide suggestions to the userdevice 110 of ordering tea instead of coffee, ordering coffee fromfulfillment center B, and ordering coffee for 9:10 AM.

The server 120 may determine the multiple alternate suggestions toprovide the user device 110 based on selecting a predetermined number ofcandidate alternate suggestions as the multiple alternate suggestions.For example, the server 120 may generate ten candidate multiplesuggestions, and determine to select three of the candidate alternatesuggestions as the multiple suggestions.

The server 120 may select the candidate alternate suggestions based onoutcome scores of each candidate alternate suggestion that indicate anestimated value of the candidate alternate suggestion. For example, theserver 120 may determine that a suggestion for an alternate time that islong after the original time may have a low likelihood of acceptancebased on historical information for the user, thus has a low outcomescore. In another example, the server 120 may determine that asuggestion for an alternate item has a moderate likelihood of acceptancebased on historical information for the user 112 but will result in muchgreater profits, thus has a high outcome score.

In some implementations, in providing alternate suggestions, the server120 may place a hold according to the alternate suggestion. A hold maybe an order that is either later canceled or confirmed. For example, inresponse to determining to provide an alternate suggestion for coffee at9 AM from fulfillment center B, the server 120 may place a hold forcoffee at 9 AM for fulfillment center B for a hold time of thirtyseconds. If the server 120 receives a response from the user device 110indicating that the alternate suggestion is accepted, the server 120 mayconfirm the hold so that the order is fulfilled. If the server 120receives a response from the user device 110 indicating that thealternate suggestion is not accepted or the hold time elapses before aresponse is received from the user device 110, the server 120 may cancelthe hold.

The fulfillment center workstations 130, 140 may receive orders from theserver 120 and provide performance information to the server 120. Forexample, the fulfillment center workstation 130 may receive an order forcoffee at 9 AM from the server 120 and provide information to the server120 indicating when the order was received, when the order wascompleted, and who completed the order.

The fulfillment center workstations 130, 140 may be located on thepremises of the fulfillment center and used by employees of thefulfillment center. For example, a fulfillment center workstation of acoffee shop may be located in a kitchen of the coffee shop and used bybaristas to determine what coffees to make and when to make the coffees.In another example, fulfillment center workstations of a pizza providermay be located in delivery vehicles of the pizza provider and used bypizza deliverers to determine where to deliver each pizza or a deliveryroute for the pizzas. The delivery route may be dynamically plannedbased on types of orders and locations to which the orders will bedelivered. In yet another example, a fulfillment center workstation maybe used by a cashier of a coffee shop to place additional orders forcustomers within the coffee shop or determine whether a customer is thecorrect customer to receive coffee that is ordered for pickup at thecoffee shop.

The fulfillment center workstations 130, 140 may provide information toemployees of the fulfillment center to fulfill orders. For example, aparticular fulfillment center workstation of a coffee shop may displayan order number, a type of coffee ordered, a customer name for theorder, a place of delivery for the order, and a time of delivery for theorder.

The fulfillment center workstations 130, 140 may obtain the informationto provide employees of the fulfillment center from the orders receivedfrom the server 120. For example, the orders received from the server120 may include data that indicates an order number, a type of coffeeordered, a customer name for the order, a place of delivery for theorder, and a time of delivery for the order. In some implementations,the fulfillment center workstations 130, 140 may prioritize thefulfillment of orders. For example, the fulfillment center workstations130, 140 may prioritize the fulfillment of orders based on when theorders are to be provided to the users.

Different configurations of the system 100 may be used wherefunctionality of the user device 110, the server 120, the historicalinformation database 122, the suggestion rule database 124, the currentorder database 126, and the fulfillment center workstations 130, 140 maybe combined, further separated, distributed, or interchanged. The system100 may be implemented in a single device or distributed across multipledevices.

FIG. 2A is an example interface 210 used in managing orders. Theinterface 210 may be displayed on a user device. The interface 210includes a notification 220 that an order cannot be fulfilled andmultiple alternate suggestions 230, 232, 234, 236. The notification 220is “Sorry, coffee is not available at 9:00 AM from Fulfillment CenterA.” The alternate suggestions include “Order tea instead for 9 AM fromFulfillment Center A” 230, “Order coffee for 9 AM from FulfillmentCenter B, which is a block away instead” 232, “Order coffee for 9:10 AMinstead from Fulfillment Center A” and “Cancel” 236. In someimplementations, the interface 210 may indicate whether the suggestionis for an alternate item, an alternate fulfillment center, or analternate time. For example, the multiple alternate suggestions 230,232, 234 include the word “instead” immediately following what isalternated. In another example, the interface 210 may color code themultiple alternate suggestions by the type of alternate, e.g.,suggestions for alternate items may be outlined in orange andsuggestions for alternate times may be outlined in green, or may bold oritalicize the portion of the alternate suggestion that is alternate,e.g., tea, fulfillment center B, and 9:10 AM may be bolded.

FIG. 2B is an example interface 250 used in managing orders. Theinterface 250 may be displayed on a user device. The interface 250 mayinclude a notification 260 that an order can be fulfilled but a specialoffer is available and multiple alternate suggestions 270, 272, 274,276. The notification 260 is “Coffee is available at 9:00 AM fromFulfillment Center A, but a special offer is available.” The alternatesuggestions include “Order tea instead for 9 AM from Fulfillment CenterA with a 10% discount” 270, “Order coffee for 9 AM from FulfillmentCenter B a block away instead with a 15% discount” 272, “Order coffeefor 9:10 AM instead from Fulfillment Center A” with a 5% discount” 274and “Place original order” 276. In some implementations, the interface250 may, similarly to interface 210, indicate whether the suggestion isfor an alternate item, an alternate fulfillment center, or an alternatetime.

FIG. 3 is an interaction diagram 300 of an example interaction ofsharing information. The following describes the interaction as beingperformed by components of the system 100 that are described withreference to FIG. 1. However, the interaction may be performed by othersystems or system configurations.

Initially, the fulfillment center workstation 130 provides performanceinformation to the server (302). For example, multiple fulfillmentcenters may provide the server 120 information regarding what employeesare working, the inventory of items available at the fulfillmentcenters, the orders placed at the fulfillment centers, and when ordersare fulfilled at the fulfillment center.

The server 120 tracks the performance information of the fulfillmentcenters and stores, in a historical information database, historicalpreparation information that describes historical performance of thefulfillment center in fulfilling orders for items at the fulfillmentcenter (304). For example, the server 120 may track performance of thefulfillment center over a month and store historical preparationinformation that describes the dates and times for each pizza that wasprepared at the fulfillment center for the month.

The user device 110 later determines to provide an order to the server120 (306). For example, the user device 110 may receive user inputrequesting that the user device 110 provide the server 120 an order fora small cheese pizza for pickup at 12 PM at fulfillment center B.

The user device 110 provides an order to the server 120 (308). Forexample, the user device 110 may provide an order to the server 120 thatindicates an item of a small cheese pizza, a fulfillment time of 12 PM,and fulfillment center B to fulfill the order. In another example, theuser device 110 may provide an order to the server 120 that indicates anitem of a small cheese pizza, a current location of the user, and anindication that the user is traveling to fulfillment center B to pick upthe pizza.

The server 120 receives the order and obtains relevant historicalpreparation information (310). For example, the server 120 may receivean order for a small cheese pizza at 12 PM from fulfillment center B,and in response, obtain a portion of stored historical preparationinformation from the historical information database 122, where theportion corresponds to historical preparation information for orders atfulfillment center B for small cheese pizzas. In another example, theserver 120 may receive an order for a small cheese pizza at 12 PM fromfulfillment center B, and in response, obtain a portion of storedhistorical preparation information from the historical informationdatabase 122, where the portion corresponds to historical preparationinformation for orders at fulfillment center B for small cheese pizzasfor the current day of the week at 12 PM.

The server 120 obtains current order information (312). For example, theserver 120 may receive an order for a small cheese pizza at 12 PM fromfulfillment center B, and then obtain, from the current order database126, orders for small cheese pizzas that are to be fulfilled byfulfillment center B at 12 PM. In another example, the server 120 mayreceive an order for a small cheese pizza at 12 PM from fulfillmentcenter B, and then obtain, from the current order database 126, ordersfor baked items that are to be fulfilled by fulfillment center B at 12PM.

The server 120 determines orders that cannot be fulfilled (314). Forexample, the server 120 may determine that an order for a small cheesepizza at 12 PM from fulfillment center B cannot be fulfilled based atleast on an obtained portion of the stored historical preparationinformation that indicates that fulfillment center B can only producefive pizzas at 12 PM and obtained current order information thatindicates that five pizzas are already ordered for 12 PM fromfulfillment center B.

The server 120 provides an indication that the order cannot be fulfilledto the user device 110 (316). For example, the server 120 may providethe user device an indication of “Sorry, your order for a small cheesepizza at 12 PM from fulfillment center B cannot be fulfilled asfulfillment center B cannot produce any more pizzas for 12 PM.”

FIG. 4 is an interaction diagram 400 of an example interaction ofsharing information. The following describes the interaction as beingperformed by components of the system 100 that are described withreference to FIG. 1. However, the interaction may be performed by othersystems or system configurations.

Initially, the fulfillment center workstation 130 may provideperformance information to the server (402). For example, multiplefulfillment centers may provide the server 120 information regardingwhat employees are working, the inventory of items available at thefulfillment centers, the orders placed at the fulfillment centers, andwhen orders are fulfilled at the fulfillment center.

The server 120 tracks the performance information of the fulfillmentcenters and stores, in a historical information database, historicalpreparation information that describes historical performance of thefulfillment center in fulfilling orders for items at the fulfillmentcenter (404). For example, the server 120 may track performance of thefulfillment center over a month and store historical preparationinformation that describes the dates and times that each cup of coffeewas prepared at the fulfillment center for the month.

The server 120 receives user responses to alternate suggestions (406).For example, the server 120 may receive responses from the userindicating when a user accepted alternate suggestions of tea instead ofcoffee and when a user rejected alternate suggestions of tea instead ofcoffee.

The user device 110 later determines to provide an order to the server120 (406). For example, the user device 110 may receive user inputrequesting that the user device 110 provide the server 120 an order fora cup of coffee for pickup at 9 AM at fulfillment center A.

The user device 110 provides an order to the server 120 (412). Forexample, the user device 110 may provide an order to the server 120 thatindicates an item of a cup of coffee, a fulfillment time of 9 AM, andfulfillment center A to fulfill the order. In another example, the userdevice 110 may provide an order to the server 120 that indicates an itemof a cup of coffee, a current location of the user, fulfillment centerA, and an indication that the user is traveling to fulfillment center Ato pick up the cup of coffee.

The server 120 receives the order and obtains relevant historicalpreparation information (414). For example, the server 120 may receivean order for a cup of coffee at 9 AM from fulfillment center A, and inresponse, obtain a portion of stored historical preparation informationfrom the historical information database 122, where the portioncorresponds to historical preparation information for orders atfulfillment center A for coffee. In another example, the server 120 mayreceive an order for a cup of coffee at 9 AM from fulfillment center A,and in response, obtain a portion of stored historical preparationinformation from the historical information database 122, where theportion corresponds to historical preparation information for orders atfulfillment center A for cups of coffee for the current day of the weekat 9 AM.

The server 120 obtains current order information (416). For example, theserver 120 may receive an order for a cup of coffee at 9 AM fromfulfillment center A, and then obtain, from the current order database126, orders for cups of coffee that are to be fulfilled by fulfillmentcenter A at 9 AM. In another example, the server 120 may receive anorder for a cup of coffee at 9 AM from fulfillment center A, and thenobtain, from the current order database 126, orders for hot beveragesthat are to be fulfilled by fulfillment center A at 9 AM.

The server 120 determines to provide an alternate suggestion (418). Forexample, the server 120 may determine that the order cannot be fulfilledand in response, identifies an alternate item, alternate fulfillmentcenter, or alternate time. In another example, the server 120 maydetermine that the order can be fulfilled but that an alternatesuggestion may result in a more valuable outcome.

The server 120 generates the alternate suggestion (420). For example,the server 120 may generate the alternate suggestion, “Order tea insteadfor 9 AM from fulfillment center A.” In another example, the server 120may generate the alternate suggestion, “Order coffee instead for 9 AMfrom fulfillment center B a block away with a 15% discount.”

The server 120 provides the alternate suggestion to the user device 110(422). For example, the server 120 may provide the alternate suggestion“Order tea instead for 9 AM from fulfillment center A” to the userdevice 110.

FIG. 5 is a flowchart of an example process 500 for managing orders. Thefollowing describes the process 500 as being performed by components ofthe system 100 that are described with reference to FIG. 1. However, theprocess 500 may be performed by other systems or system configurations.

The server 120 tracks performance of a fulfillment center (510). Forexample, the server 120 may track the time it takes for fulfillmentcenter B to fulfill different orders.

The server 120 stores historical preparation information (520). Forexample, the server 120 stores historical preparation information thatdescribes when each order was received, what each order was for, and howlong fulfillment center B took to complete each order.

The server 120 receives an order for an item to be fulfilled by thefulfillment center for a fulfillment time (530). For example, the server120 receives from user device 110 an order for coffee to be fulfilled byfulfillment center B for 9 AM. In another example, the server 120receives from the user device 110 an order that indicates coffee, acurrent location of the user, and a destination of the user, and theserver 120 determines fulfilment centers accessible along the route ofthe user and the times that the user will pass by the fulfillmentcenters.

The server 120 may obtain a portion of the stored historical preparationinformation (540). For example, the server 120 may obtain an order forcoffee to be fulfilled by fulfillment center B for 9 AM, and inresponse, obtain a portion of stored historical preparation informationfor coffee previously fulfilled by fulfillment center B for 9 AM. Inanother example, the server 120 may obtain an order for coffee,determine fulfillment centers accessible to a user along a user's routeand times that the user will be near each fulfillment center, and inresponse, obtain a portion of stored historical preparation informationfor coffee previously prepared by the fulfillment centers at the timesthat the user will be near the fulfillment centers.

The server 120 may obtain current order information (550). For example,the server 120 may obtain a list of orders to be fulfilled byfulfillment center B. In another example, the server 120 may obtain alist of orders for fulfillment centers that the user will be near wherethe orders are for times that the user will be near the correspondingfulfillment centers.

The server 120 may determine whether a fulfillment center can fulfillthe order by the fulfillment time (560). For example, the server 120 maydetermine that fulfillment center B cannot fulfill the order for coffeeat 9 AM based at least on the portion of stored historical preparationinformation indicating nine cups of coffee can be provided byfulfillment center B at 9 AM and current order information indicatingthat nine cups of coffee have already been ordered from fulfillmentcenter B at 9 AM. In another example, the server 120 may determine thatan order for coffee at 9 AM cannot be fulfilled based at least on theportion of stored historical preparation information indicating a numberof cups of coffee that can be produced by different fulfillment centersalong a user's route, and current order information indicating a numberof cups of coffee ordered from the fulfillment centers.

FIG. 6 is a flowchart of an example process 600 for managing orders. Thefollowing describes the process 600 as being performed by components ofthe system 100 that are described with reference to FIG. 1. However, theprocess 600 may be performed by other systems or system configurations.

The server 120 tracks performance of a fulfillment center and userresponses to alternate suggestions (610). For example, the server 120may track that it takes fulfillment center B ten minutes to preparegrilled cheese sandwich, and the user has accepted an alternatesuggestion of a cheeseburger instead of a grilled cheese sandwich, andhas rejected alternate suggestions of jam and toast instead of a grilledcheese sandwich.

The server 120 stores historical preparation information and historicaluser information (620). For example, the server 120 stores historicalpreparation information that describes that on average fulfillmentcenter B takes ten minutes to prepare a grilled cheese sandwich, andstores historical user information that indicates that the user hasaccepted a cheeseburger as an alternate item to a grilled cheesesandwich and has rejected alternate suggestions of jam and toast insteadof a grilled cheese sandwich.

The server 120 receives an order for an item to be fulfilled by thefulfillment center for a fulfillment time (630). For example, the server120 receives from user device 110 an order for a grilled cheese sandwichto be fulfilled by fulfillment center B for 11 AM.

The server 120 obtains a portion of the stored historical preparationinformation (640). For example, the server 120 may obtain an order for agrilled cheese sandwich to be fulfilled by fulfillment center B for 11AM, and in response, obtain a portion of stored historical preparationinformation that indicates that the fulfillment center B takes about tenminutes to prepare a grilled cheese sandwich.

The server 120 obtains current order information (650). For example, theserver 120 may obtain a list of orders to be fulfilled by fulfillmentcenter B. In another example, the server 120 may obtain a list of ordersfor grilled cheese sandwiches to be fulfilled by fulfillment center B.In yet another example, the server 120 may obtain a list of orders forgrilled cheese sandwiches to be fulfilled by fulfillment center B at 11AM.

The server 120 determines to provide an alternate suggestion (660). Forexample, the server 120 may determine that the order for a grilledcheese sandwich to be fulfilled by fulfillment center B for 11 AM cannot be fulfilled. In another example, the server 120 may determine thatthe order for a grilled cheese sandwich to be fulfilled by fulfillmentcenter B for 11 AM can be fulfilled, but the user is likely to accept analternate suggestion of a later fulfillment time of 11:30 AM, thatotherwise another user would not be able to order a grilled cheesesandwich for 11 AM, that another user is likely to order a grilledcheese sandwich for 11 AM, and that the other user is not likely toaccept an alternate suggestion.

The server 120 identifies at least one of an alternate item, analternate fulfillment center, or an alternate time (670). For example,the server 120 may determine from stored historical user information toidentify a cheeseburger as an alternate item to a grilled cheesesandwich based on determining that the user has previously ordered acheeseburger. In another example, the server 120 may determine fromstored historical user information to identify a cheeseburger as analternate item to a grilled cheese sandwich based on determining thatthe user has previously accepted an alternate suggestion of acheeseburger instead of a grilled cheese sandwich. In yet anotherexample, the server 120 may determine from stored historical userinformation to identify a cheeseburger as an alternate item to a grilledcheese sandwich based on determining that the user has not previouslyrejected an alternate suggestion of a cheeseburger instead of a grilledcheese sandwich.

The server 120 generates an alternate suggestion based on theidentification (680). For example, in response to identifying acheeseburger as an alternate item to a grilled cheese sandwich, theserver 120 may generate an alternate suggestion of “Sorry a grilledcheese sandwich is not available from fulfillment center B for 11 AM.Would you like to instead order a cheeseburger?”

The server 120 provides the alternate suggestion to the user device(690). For example, server 120 provides the alternate suggestion “Sorrya grilled cheese sandwich is not available from fulfillment center B for11 AM. Would you like to instead order a cheeseburger?” to the userdevice 110.

FIG. 7 is a block diagram of an example system 700. Briefly, and asdescribed in further detail below, the system 700 may include a locationdeterminator 702, smart appliances 710, a mobile application 712, a webapplication 714, a text or short message service (SMS) application 716,a voice interface 718, an ordering backend 720, call center workstations722, local fulfillment center workstations 730, and a supply chain,finance, or enterprise component 740. The system 700 may provide forreal time delivery of an item. Real time delivery may includepoint-to-point delivery of small, tangible, purchased goods within avery short period of time of purchase, e.g., few minutes to a few hours.Purchased goods may include typical packaged goods that do not otherwiserequire real-time delivery, e.g., consumer electronics or officesupplies, goods that degrade over a long period of time, e.g., groceriesor flowers, or goods that degrade within a very short period of time,e.g., dry ice, ice cream, hot pizza or a cup of coffee.

The location determinator 702 may determine location information for acustomer. For example, the location determinator 702 may determine acustomer's location based on or more of a service set identifier (SSID)of a nearby detected wireless network, a detected phone mast signal,determined global positioning system (GPS) information, an assignedInternet Protocol (IP) address, a landline location, or a wireless mediaaccess control (MAC) address. The location determinator 702 may providethe location information to the smart appliances 710, the mobileapplication 712, the web application 714, the text or short messageservice (SMS) application 716, and the voice interface 718. In someimplementations, the location determinator 702 may be included in theuser device 110 of FIG. 1.

The smart appliances 710, the mobile application 712, the webapplication 714, the text or short message service (SMS) application716, and the voice interface 718 may enable a customer to place anorder. For example, one or more of the smart appliances 710, the mobileapplication 712, the web application 714, the text or short messageservice (SMS) application 716, and the voice interface 718 may enable acustomer to place an order for a cheese pizza to be delivered to thecustomer's home address at 6:00 PM. To enable customers to place orders,the smart appliances 710, the mobile application 712, the webapplication 714, the text or short message service (SMS) application716, and the voice interface 718 may provide interfaces through whichthe customer may specify a particular item to order, and a particularlocation for which the customer would like to obtain the item. Forexample, the smart appliances 710, the mobile application 712, the webapplication 714, the text or short message service (SMS) application716, and the voice interface 718 may visually or audibly provide promptsto the customer requesting that the customer specify a particular itemto order, a particular location to receive the item, and a particulartime to receive the item.

More specifically, the smart appliances 710 may be appliances that maybe used to place orders. For example, the smart appliances 710 mayinclude a television that may be used by a customer to place an orderfor a pizza. The smart appliances 710 may provide an interface for acustomer to place an order. For example, the smart appliance 710 mayinclude a display that may render an interface for the customer to ordera pizza. The smart appliances 710 may provide orders to an orderingbackend 720. For example, the smart appliances 710 may provide an orderfor a pizza through a network to the ordering backend 720.

The mobile application 712 may be an application running on a mobiledevice that enables a customer to place an order. For example, anapplication running on a customer's smart phone may enable the customerto place an order for pizza. A mobile device may include a smart phone,a tablet computer, or some other type of computing device that may beportably used. The mobile application 712 may provide an interface for acustomer to place an order. For example, the mobile application 712 maydisplay on the mobile device an interface for the customer to order apizza. The mobile application 712 may provide orders to an orderingbackend 720. For example, the mobile application 712 may provide anorder for a pizza through a network to the ordering backend 720. In someimplementations, the mobile application 712 may be an applicationinstalled on the user device 110 of FIG. 1.

The web application 714 may be an application accessible through a webbrowser running on a computing device, where the application enables acustomer to place an order. For example, a Java application may berendered by a web browser on a desktop computer and enable a customer toplace an order for pizza. A computing device may include a mobiledevice, a desktop computer, a laptop computer, or some other type ofdevice that computes. The web application 714 may provide an interfacefor a customer to place an order. For example, the web application 714may display an interface, in a web browser on a desktop computer, forthe customer to order a pizza. The web application 714 may provideorders to an ordering backend 720. For example, the web application 714may provide an order for a pizza through a network to the orderingbackend 720. In some implementations, the web application 714 may beaccessed by a web browser on the user device 110 of FIG. 1.

The text or SMS application 716 may be an application running on amobile device that enables a customer to place an order by text or SMS.For example, an application running on a customer's smart phone mayenable the customer to send a text or SMS message to the orderingbackend 720 to place an order for pizza. In some implementations, theapplication may provide a graphical user interface for the user toselect a particular item, particular location, and a particular time toreceive the item, and generate a SMS message indicating the selectionsand provide the SMS message to the ordering backend 720. In someimplementations, the test or SMS application 716 may be running on theuser device 110 of FIG. 1.

The voice interface 718 may be an interface that enables a customer toplace an order by voice. For example, the voice interface 718 may be atelephone that a customer may use to verbally order a pizza. The voiceinterface 718 may communicate with the call center workstations 722. Forexample, the voice interface 718 may transmit audio captured by thevoice interface 718 to the call center workstations 722, and similarly,output audio from the call center workstations 722. The voice interface718 may additionally or alternatively communicate with a localvoice-based order taking component 132 of a local fulfillment centerworkstation 730. For example, the voice interface 718 may transmit audiocaptured by the voice interface 718 to the local voice-based ordertaking component 132, and similarly, output received audio from thelocal voice-based order taking component 132. In some implementations,the voice interface 718 may be included in the user device 110 of FIG.1.

The call center workstations 722 may receive voice input from the voiceinterface 718, generate orders based on the voice input, and provide theorders to the ordering backend 720. For example, the call centerworkstations 722 may audibly output voice input from the interface 718to allow customer service representatives to verbally interact withcustomers, and generate orders for the customers. In someimplementations, the call center workstations 722 may be automated anduse speech recognition to generate orders for users. For example, thecall center workstations 722 may execute an Artificial Intelligencecustomer service agent program that may verbally speak to the customerto take the order and place the order directly for the customer.

The ordering backend 720 may be a component that processes orders fromcustomers. For example, the ordering backend may receive orders from thesmart appliances 710, mobile application 712, web application 714, textor SMS application 716, the voice interface 718, or the call centerworkstations 722. The ordering backend 720 may process orders based ondetermining, for each order, one or more local fulfillment centerworkstations 730 to receive the order. For example, the ordering backend720 may determine that a first order for a pizza should be provided to afirst local fulfillment center workstation and a second order for apizza should be provided to a different, second local fulfillment centerworkstation. In some implementations, the ordering backend 720 may bethe server 120 of FIG. 1.

The ordering backend 720 may determine a local fulfillment centerworkstation to receive a particular order based on a location which thecustomer would like to obtain the item ordered. For example, theordering backend 720 may determine that a first order indicates theorder is to be obtained at a location where the distance from thelocation to the local fulfillment center is within a distance threshold,and in response, determine to provide the first order to a workstationof the local fulfillment center. In another example, the orderingbackend 720 may determine that a second order is to be obtained at alocation where the distance from the location to the local fulfillmentcenter exceeds a distance threshold, and in response, determine thesecond order should be provided to a workstation of another localfulfillment center.

Additionally or alternatively, the ordering backend 720 may determine alocal fulfillment center workstation to receive a particular order basedon availability of the local fulfillment center to fulfill the order.For example, the ordering backend 720 may determine that a first orderfor a pizza can be fulfilled by a local fulfillment center as the localfulfillment center may still have capacity to provide the pizza. Inanother example, the ordering backend 720 may determine that a secondorder for a pizza cannot be fulfilled by a local fulfillment center asthe local fulfillment center may not have capacity to provide the pizza.

In response to determining the local fulfillment center workstations 730to receive an order, the ordering backend 720 may provide the order tothe local fulfillment center workstations 730. For example, in responseto determining that a local fulfillment center does deliver to theparticular location requested by a customer and has capacity to fulfillan order for pizza, the ordering backend 720 may determine to providethe order for pizza to the local fulfillment center workstations 730 ofthe local fulfillment center.

In processing the orders, the ordering backend may also process paymentsfor the orders. For example, the ordering backend 720 may validatewhether payment information provided in the order is correct. In anotherexample, the ordering backend 720 may place charges for orders usingpayment information provided in the order.

The ordering backend 720 may include a bulk order processor 724. Thebulk order processor 724 may enable the ordering backend 720 to performmass processing of orders. For example, the ordering backend 720 mayreceive an extremely large number of orders within a given time framefrom the smart appliances 710, mobile application 712, web application714, text or SMS application 716, the voice interface 718, or the callcenter workstations 722. Processing such an amount of orders may includedetermining, for each order, one or more local fulfillment centerworkstations out of thousands of potential local fulfillment centerworkstations 730 to receive the order.

The ordering backend 720 may also include a bulk order router 726. Thebulk order router 726 may enable the ordering backend 720 to performmass management of logistics, including delivery scheduling andcollection scheduling of received orders. For example, the orderingbackend may receive an extremely large number of orders within a giventime frame from the smart appliances 710, mobile application 712, webapplication 714, text or SMS application 716, the voice interface 718,or the call center workstations 722. Managing the delivery scheduling orcollection scheduling of such an amount of orders may includedetermining an order preparation schedule for each local fulfillmentcenter workstation.

The ordering backend 720 performs a variety of tasks relating toprocessing or routing bulk orders, including sorting, assigning,routing, queuing, distributing and scheduling, to name a few. The study,optimization and execution of these tasks requires the uses oftechniques and results from well-developed, active areas of scientificresearch, such as operational research, combinatorial optimization,graph theory (in particular network theory), queuing theory, andtransport theory. Executing or providing optimal solutions to such tasksare proven to be complex and mathematically hard, particularly whenconsidering large-scale systems. For example, e-commerce giant Amazonreported that they received, processed and delivered orders for 426items per second in the run up to Christmas in 2013, see for examplehttp://articles.latimes.com/2013/dec/26/business/la-fi-tn-amazon-sold-426-items-per-second-cyber-monday-20131226.

Since scaling is an extremely important factor, particularly in thecontext of e-commerce, tasks relating to processing or routing bulkorders quickly become extremely difficult, if not impossible, to solveand require powerful computers implementing complex algorithms andelegant mathematical programming techniques, as well as robust hardwarearchitectures that can be parallelized. In some cases, it has even beenshown that many tasks relating to the processing or routing of bulkorders are computationally intractable. In fact, the theory ofcomputational complexity has introduced a concept of NP-hardness toclassify such computationally intractable tasks.

For example, consider the intensely studied Traveling Salesman Problem(TSP), an NP-hard problem in combinatorial optimization. In its purestformulation, the TSP may be described as follows: given a list of citiesand the distances between each pair of cities, what is the shortestroute that visits each city exactly once, and returns to the originalcity. The TSP may also be modeled and described as a graph problem,wherein the cities are the graph's vertices and the distances betweeneach pair of cities are the lengths of the graph's edges, or formulatedas an integer linear program. The TSP has several applications inoperational research, including planning and logistics, wherein theconcept of a city may represent customers or orders, and the concept ofa distance may represent travelling times or incurred costs. In someimplementations, additional constraints may be imposed on the TSP, suchas limiting an amount of resources or limiting to certain time windows.Finding increasingly efficient solutions to the TSP and other suchcomplex, subtle operational research problems is an active area ofscientific research, as evidenced by a plethora of technical textbooks,journals and scientific articles. See, for example “In Pursuit of theTravelling Salesman: Mathematics at the Limits of Computation,” WilliamCook, Princeton University Press, 2011, “Dynamic programming strategiesand reduction techniques for the traveling salesman problem with timewindows and precedence constraints,” L. Bianco, A. Mingozzi, and S.Ricciardelli, Operations Research, 45 (1997) 365-377 and “Schedulingvehicles from a central depot to a number of delivery points,” G. Clarkeand J. Wright, Operations Research 12 (1964) 568-581.

Another well-studied, fundamental combinatorial optimization problem inthe field of operational research and optimization is the AssignmentProblem (AP). In its most general form, the AP may be described asfollows: given a number of agents, a number of tasks and a set ofincurring costs for each potential agent-task assignment, perform alltasks by assigning one agent to each task, and one task to each agent,such that the total costs incurred is minimized. The AP also has severalapplications in areas such as planning and logistics. For example, theordering backend 720 may interpret the task of processing receivedorders as an assignment problem, wherein a local fulfillment centerworkstation can be assigned to deliver an order, incurring some cost.The bulk order processor 724, for example, may require that all ordersare delivered by assigning one local fulfillment center to each order,and one order to each local fulfillment center, in such a way that thetotal cost of the assignment is minimized. It has been shown that suchan assignment problem can be expressed mathematically as a standardlinear program, see, for example “Algorithms for the Assignment andTransportation Problems,” James Munkres, Journal of the Society forIndustrial and Applied Mathematics Vol. 5, No. 1 (March, 1957), pp.32-38.

Linear programming has been shown to be an extremely powerful, essentialoptimization technique widely applied in various fields of study such asbusiness studies and economics, as well as industrial areas such asengineering, transportation and manufacturing. The sophisticatedmathematical programming techniques used in linear programming haveproven essential to the modeling of diverse types of problems inrouting, scheduling and assignment, and in turn have led to the creationof powerful computer systems that enable businesses to reduce costs,improve profitability, use resources effectively, reduce risks andprovide untold benefits in many other key dimensions. Mathematically,linear programming is a technique for the optimization of a linearobjective function, subject to linear equality and linear inequalityconstraints. While this requirement may seem overly restrictive, manyreal-world business problems can be formulated in this manner. Today,commercial linear-programming codes are able to solve linear programswith millions of constraints. For example, the commercial,high-performance mathematical programming solver IBM ILOG CPLEX of IBMis able to solve linear programs with the number of megabytes requiredfor computation approximately equal to the number of constraints dividedby 1000, see http://www-01.ibm.com/support/docview.wss?uid=swg21399933.

When dealing with large-scale systems, system components, such as theordering backend 720, may employ techniques and results from QueuingTheory—the mathematical study of waiting lines, or queues—in order tomodel incoming customers or orders and subsequently make businessdecisions relating to order fulfillment, resource requirements forproviding a certain service, expected waiting time in a queue, averagetime in a queue, expected queue length, expected number of customers ina queue at a given time, and the probability of a system to be in acertain state, such as empty or full. For example, it has been shownthat most queues in restaurant operations can be described by an M/M/1queue, where arrivals are determined by a Poisson process with heavyperiods around lunch and dinner time, and service times have anexponential distribution. In a complex system of multiple M/M/1 queues,or a queue network, deep and sophisticated mathematical techniques suchas stochastic calculus may be employed to model or approximate thequeuing process. For example, in some implementations a heavy trafficapproximation may be used to approximate a queuing process by areflected Brownian motion, Ornstein-Uhlenbeck process or more generaldiffusion process. Since queues are basic components to both externaland internal business processes, including scheduling and inventorymanagement, understanding the nature of queues and learning how tomanage them is one of the most important areas in operational researchand management. See, for example, “A Methodology and Implementation forAnalytic Modeling in Electronic Commerce Applications,” H. Edwards, M.Bauer, H. Lutfiyya, Y. Chan, M. Shields and P. Woo, Electronic CommerceTechnologies, Lecture notes in computer science, Volume 2040, 2001, pp148-157 and “Stochastic Models in Queuing Theory”, J. Medhi, ElsevierAcademic Press, 2002.

The local fulfillment center workstations 730 may receive orders fromthe ordering backend 720. For example, a particular local fulfillmentcenter workstation may receive an order for pizza from the orderingbackend 720. The local fulfillment center workstations may be located onthe premises of the local fulfillment center and used by agents of thelocal fulfillment center. For example, a local fulfillment centerworkstation of a pizza provider may be located in a kitchen of the pizzaprovider and used by bakers to determine what pizzas to bake and when tobake the pizzas. In another example, local fulfillment centerworkstations of a pizza provider may be located in delivery vehicles ofthe pizza provider and used by pizza deliverers to determine where todeliver each pizza or a delivery route for the pizzas. The deliveryroute may be dynamically planned based on types of orders and locationsto which the orders will be delivered. In yet another example, a localfulfillment center workstation may be used by a cashier of a pizzaprovider and used by the cashier to place additional orders forcustomers within the restaurant or determine whether a customer is thecorrect customer to receive a pizza that is ordered for pickup at therestaurant. In some implementations, the local fulfillment centerworkstations 730 may be the fulfillment center workstations 130, 140 ofFIG. 1.

The local fulfillment center workstations 730 may provide information toagents of the local fulfillment center to fulfill orders. For example, aparticular local fulfillment center workstation of a pizza provider maydisplay an order number, a type of pizza ordered, a customer name forthe order, a place of delivery for the order, and a time of delivery forthe order. The local fulfillment center workstations 730 may obtain theinformation to provide agents of the local fulfillment center from theorders received from the ordering backend 720. For example, the ordersreceived from the ordering backend 720 may include data that indicatesan order number, a type of pizza ordered, a customer name for the order,a place of delivery for the order, and a time of delivery for the order.In some implementations, the local fulfillment center workstations 730may prioritize the fulfillment of orders. For example, the localfulfillment center workstations 730 may prioritize the fulfillment oforders based on when the orders are to be provided to the users.

The local fulfillment center workstations 730 may include a local,voice-based order taking component 132. For example, the localfulfillment center workstations 730 may include a telephone-based systemwith which an employee of the local fulfillment center may speak to thecustomer. The local, voice-based order taking component 132 may enablecustomers to place orders directly with the local fulfillment center.For example, the same local fulfillment center workstations 730 thatreceive orders from the ordering backend 720 may also be used togenerate orders based on input from customer service agents using thelocal fulfillment center workstations 730. In some implementations, thelocal, voice-based order taking component 132 may be automated and usespeech recognition to generate orders for users.

The local fulfillment center workstations 730 may additionally oralternatively communicate with a supply chain or finance or enterprisecomponent of a third party. For example, a local fulfillment centerworkstation may place an order for more flour with a supply chaincomponent of a third party that sells flour for pizza. In anotherexample, a local fulfillment center workstation may communicate with afinance component to place payment charges for orders.

The local fulfillment center workstations 730 may automaticallydetermine to communicate with the supply chain or finance or enterprisecomponent. For example, the local fulfillment center workstations 730may automatically determine that the amount of flour in the inventory ofthe local fulfillment center is below a flour threshold, and inresponse, place an order for flour to the supply chain component of apizza flour provider.

FIG. 8 illustrates a schematic diagram of an exemplary generic computersystem. The system 800 can be used for the operations described inassociation with the processes 500 and 600 according to someimplementations. The system 800 may be included in the system 100.

The system 800 includes a processor 810, a memory 820, a storage device830, and an input/output device 840. Each of the components 810, 820,830, and 820 are interconnected using a system bus 850. The processor810 is capable of processing instructions for execution within thesystem 800. In one implementation, the processor 810 is asingle-threaded processor. In another implementation, the processor 810is a multi-threaded processor. The processor 810 is capable ofprocessing instructions stored in the memory 820 or on the storagedevice 830 to display graphical information for a user interface on theinput/output device 840.

The memory 820 stores information within the system 800. In oneimplementation, the memory 820 is a computer-readable medium. In oneimplementation, the memory 820 is a volatile memory unit. In anotherimplementation, the memory 820 is a non-volatile memory unit.

The storage device 830 is capable of providing mass storage for thesystem 800. In one implementation, the storage device 830 is acomputer-readable medium. In various different implementations, thestorage device 830 may be a floppy disk device, a hard disk device, anoptical disk device, or a tape device.

The input/output device 840 provides input/output operations for thesystem 800. In one implementation, the input/output device 840 includesa keyboard and/or pointing device. In another implementation, theinput/output device 840 includes a display unit for displaying graphicaluser interfaces.

Embodiments of the subject matter, the functional operations and theprocesses described in this specification can be implemented in digitalelectronic circuitry, in tangibly-embodied computer software orfirmware, in computer hardware, including the structures disclosed inthis specification and their structural equivalents, or in combinationsof one or more of them. Embodiments of the subject matter described inthis specification can be implemented as one or more computer programs,i.e., one or more modules of computer program instructions encoded on atangible nonvolatile program carrier for execution by, or to control theoperation of, data processing apparatus. Alternatively or in addition,the program instructions can be encoded on an artificially generatedpropagated signal, e.g., a machine-generated electrical, optical, orelectromagnetic signal that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus. The computer storage medium can be amachine-readable storage device, a machine-readable storage substrate, arandom or serial access memory device, or a combination of one or moreof them.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application specificintegrated circuit). The apparatus can also include, in addition tohardware, code that creates an execution environment for the computerprogram in question, e.g., code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, or acombination of one or more of them. In some cases, the one or moreprogrammable computers may be connected by a network to form adistributed computing environment (e.g., a cloud).

A computer program (which may also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code) can be written in any form of programming language,including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astandalone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program may, butneed not, correspond to a file in a file system. A program can be storedin a portion of a file that holds other programs or data (e.g., one ormore scripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or portionsof code). A computer program can be deployed to be executed on onecomputer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Computers suitable for the execution of a computer program include, byway of example, can be based on general or special purposemicroprocessors or both, or any other kind of central processing unit.Generally, a central processing unit will receive instructions and datafrom a read-only memory or a random access memory or both. The essentialelements of a computer are a central processing unit for performing orexecuting instructions and one or more memory devices for storinginstructions and data. Generally, a computer will also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio or video player, a game console, a Global PositioningSystem (GPS) receiver, or a portable storage device (e.g., a universalserial bus (USB) flash drive), to name just a few.

Computer readable media suitable for storing computer programinstructions and data include all forms of nonvolatile memory, media andmemory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto optical disks; andCD-ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of what may beclaimed, but rather as descriptions of features that may be specific toparticular embodiments. Certain features that are described in thisspecification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

What is claimed is:
 1. A computer implemented method, comprising:tracking, over time and by bulk order processing server of an orderprocessing system that includes (i) the bulk order processing server,(ii) local fulfillment center workstations that are each associated witha different third party fulfillment center, and (iii) multiple userdevices, performance of the fulfillment center in fulfilling orders foritems at the fulfillment center based on data collected at the localfulfillment center workstations, and user responses to alternatesuggestions based on data collected at the multiple user devices; basedon the tracking by the bulk order processing server, storing, in anelectronic database accessible to the bulk order processing server ofthe order processing system, historical preparation information thatdescribes historical performance of the fulfillment center in fulfillingorders for items at the fulfillment center based on the data collectedat the local fulfillment center workstations; based on the tracking bythe bulk order processing server, storing, in the electronic databaseaccessible to the bulk order processing server of the order processingsystem, historical user information that indicates a likelihood that auser indicate acceptance of an alternate suggestion at one of the userdevices; receiving, over a network, by the bulk order processing server,and from one of the user associated with the user and the orderprocessing system, an order for an item to be fulfilled by a particularlocal fulfillment center for a fulfillment time, where the orderindicates the item and the particular fulfillment center fulfills theorder by enabling an employee at the fulfillment center to prepare theitem for the user, wherein the particular user device is a mobilecomputing device and the order is entered through a graphical userinterface of a web application presented on the mobile computing device;in response to receiving the order from the user device: obtaining, fromthe database accessible to the bulk order processing server and based atleast on the indication in the order of the fulfillment center, aportion of the stored historical preparation information for thefulfillment center that describes historical performance of thefulfillment center that is relevant to fulfillment of the item by thefulfillment time; obtaining, based at least on the indication in theorder of the fulfillment center, current order information thatdescribes other orders to be fulfilled at the fulfillment center; anddetermining, by the bulk order processing server, to provide analternate suggestion for the user device to provide to the user throughthe graphical user interface of the web application based at least on aportion of the stored historical preparation information and the currentorder information; in response to determining to provide the alternatesuggestion for the user device to provide to the user through thegraphical user interface of the web application, identifying, by thebulk order processing server, at least one of an alternate item, analternate fulfillment center, or an alternate time to include in thealternate suggestion based on the stored historical user informationthat indicates a likelihood that the user will indicate acceptance of analternate suggestion at one of the user devices and further based atleast on historical performance of the alternate fulfillment center,current orders for the alternate fulfillment center, historicalperformance for the alternate item, current orders for the alternateitem, or current orders for the alternate time; generating, by the bulkorder processing server, the alternate suggestion based on theidentification of at least one of the alternate item, the alternatefulfillment center, or the alternate time to include in the alternatesuggestion; and providing, over the network and by the bulk orderprocessing server, instructions that trigger the graphical userinterface of the web application on the user device to (i) provide thealternate suggestion to the user without the user providing furtherinput after the user enters the order through the graphical userinterface of the web application and (ii) disable entering, in thegraphical user interface of the web application, a request for the itemfrom the fulfillment center.
 2. The method of claim 1, whereindetermining, to provide an alternate suggestion for the user device toprovide to the user through the graphical user interface of the webapplication based at least on a portion of the stored historicalpreparation information and the current order information comprises:determining that the order cannot be fulfilled by the fulfillment centerfor the fulfillment time.
 3. The method of claim 1, wherein determining,to provide an alternate suggestion for the user device to provide to theuser through the graphical user interface of the web application basedat least on a portion of the stored historical preparation informationand the current order information comprises: determining that the ordercan be fulfilled by the fulfillment center for the fulfillment time butanother order is likely to be placed for the item for the fulfillmenttime.
 4. The method of claim 1, wherein determining, to provide analternate suggestion for the user device to provide to the user throughthe graphical user interface of the web application based at least on aportion of the stored historical preparation information and the currentorder information comprises: determining that the alternate suggestionis likely to result in a better business value for an entity providingthe server.
 5. The method of claim 1, identifying at least one of analternate item, an alternate fulfillment center, or an alternate time toinclude in the alternate suggestion based on the stored historical userinformation that indicates a likelihood that the user will accept thealternate suggestion comprises: determining that the alternatesuggestion can be fulfilled.
 6. The method of claim 1, whereinidentifying at least one of an alternate item, an alternate fulfillmentcenter, or an alternate time to include in the alternate suggestionbased on the stored historical user information that indicates alikelihood that the user will accept the alternate suggestion comprises:determining that the user is likely to be interested in the alternateitem based on the stored historical user information that indicates alikelihood that the user will accept the alternate suggestion; and inresponse to determining that the user is likely to be interested in thealternate item based on the stored historical user information thatindicates a likelihood that the user will accept the alternatesuggestion, identifying the alternate item.
 7. The method of claim 1,wherein identifying at least one of an alternate item, an alternatefulfillment center, or an alternate time to include in the alternatesuggestion based on the stored historical user information thatindicates a likelihood that the user will accept the alternatesuggestion comprises: determining a likely route for the user based atleast on the stored historical user information that indicates alikelihood that the user will accept the alternate suggestion;determining that the alternate fulfillment center is accessible alongthe likely route; and in response to determining that the alternatefulfillment center is accessible along the likely route, identifying thealternate fulfillment center.
 8. The method of claim 1, whereinidentifying at least one of an alternate item, an alternate fulfillmentcenter, or an alternate time to include in the alternate suggestionbased on the stored historical user information that indicates alikelihood that the user will accept the alternate suggestion comprises:determining an earliest time after the fulfillment time that the ordercan be fulfilled; determining whether the earliest time satisfies a timethreshold with the fulfillment time; and in response to determining thatthe earliest time satisfies the time threshold with the fulfillmenttime, identifying the earliest time as the alternate time.
 9. The methodof claim 1, wherein generating the alternate suggestion based on theidentification of at least one of the alternate item, the alternatefulfillment center, or the alternate time to include in the alternatesuggestion comprises: determining a discount to include in the alternatesuggestion; and including the discount in the alternate suggestion. 10.The method of claim 1, comprising: reserving an order for the alternatesuggestion for a predetermined time before receiving a response from theuser device to the alternate suggestion.
 11. The method of claim 1,wherein generating the alternate suggestion based on the identificationof at least one of the alternate item, the alternate fulfillment center,or the alternate time to include in the alternate suggestion comprises:generating, for each of multiple candidate alternate suggestions,outcomes scores that each indicate an estimated value of the candidatealternate suggestion; and selecting a predetermined number of thecandidate alternate suggestions based at least on the outcome scores.12. A system comprising: one or more computers and one or more storagedevices storing instructions that are operable, when executed by the oneor more computers, to cause the one or more computers to performoperations comprising: tracking, over time and by bulk order processingserver of an order processing system that includes (i) the bulk orderprocessing server, (ii) local fulfillment center workstations that areeach associated with a different third party fulfillment center, and(iii) multiple user devices, performance of the fulfillment center infulfilling orders for items at the fulfillment center based on datacollected at the local fulfillment center workstations, and userresponses to alternate suggestions based on data collected at themultiple user devices; based on the tracking by the bulk orderprocessing server, storing, in an electronic database accessible to thebulk order processing server of the order processing system, historicalpreparation information that describes historical performance of thefulfillment center in fulfilling orders for items at the fulfillmentcenter based on the data collected at the local fulfillment centerworkstations; based on the tracking by the bulk order processing server,storing, in the electronic database accessible to the bulk orderprocessing server of the order processing system, historical userinformation that indicates a likelihood that a user indicate acceptanceof an alternate suggestion at one of the user devices; receiving, over anetwork, by the bulk order processing server, and from one of the userassociated with the user and the order processing system, an order foran item to be fulfilled by a particular local fulfillment center for afulfillment time, where the order indicates the item and the particularfulfillment center fulfills the order by enabling an employee at thefulfillment center to prepare the item for the user, wherein theparticular user device is a mobile computing device and the order isentered through a graphical user interface of a web applicationpresented on the mobile computing device; in response to receiving theorder from the user device: obtaining, from the database accessible tothe bulk order processing server and based at least on the indication inthe order of the fulfillment center, a portion of the stored historicalpreparation information for the fulfillment center that describeshistorical performance of the fulfillment center that is relevant tofulfillment of the item by the fulfillment time; obtaining, based atleast on the indication in the order of the fulfillment center, currentorder information that describes other orders to be fulfilled at thefulfillment center; and determining, by the bulk order processingserver, to provide an alternate suggestion for the user device toprovide to the user through the graphical user interface of the webapplication based at least on a portion of the stored historicalpreparation information and the current order information; in responseto determining to provide the alternate suggestion for the user deviceto provide to the user through the graphical user interface of the webapplication, identifying, by the bulk order processing server, at leastone of an alternate item, an alternate fulfillment center, or analternate time to include in the alternate suggestion based on thestored historical user information that indicates a likelihood that theuser will indicate acceptance of an alternate suggestion at one of theuser devices and further based at least on historical performance of thealternate fulfillment center, current orders for the alternatefulfillment center, historical performance for the alternate item,current orders for the alternate item, or current orders for thealternate time; generating, by the bulk order processing server, thealternate suggestion based on the identification of at least one of thealternate item, the alternate fulfillment center, or the alternate timeto include in the alternate suggestion; and providing, over the networkand by the bulk order processing server, instructions that trigger thegraphical user interface of the web application on the user device to(i) provide the alternate suggestion to the user without the userproviding further input after the user enters the order through thegraphical user interface of the web application and (ii) disableentering, in the graphical user interface of the web application, arequest for the item from the fulfillment center.
 13. The system ofclaim 12, wherein determining, to provide an alternate suggestion forthe user device to provide to the user through the graphical userinterface of the web application based at least on a portion of thestored historical preparation information and the current orderinformation comprises: determining that the order cannot be fulfilled bythe fulfillment center for the fulfillment time.
 14. The system of claim12, wherein determining, to provide an alternate suggestion for the userdevice to provide to the user through the graphical user interface ofthe web application based at least on a portion of the stored historicalpreparation information and the current order information comprises:determining that the order can be fulfilled by the fulfillment centerfor the fulfillment time but another order is likely to be placed forthe item for the fulfillment time.
 15. The system of claim 12, whereindetermining, to provide an alternate suggestion for the user device toprovide to the user through the graphical user interface of the webapplication based at least on a portion of the stored historicalpreparation information and the current order information comprises:determining that the alternate suggestion is likely to result in abetter business value for an entity providing the server.
 16. The systemof claim 12, identifying at least one of an alternate item, an alternatefulfillment center, or an alternate time to include in the alternatesuggestion based on the stored historical user information thatindicates a likelihood that the user will accept the alternatesuggestion comprises: determining that the alternate suggestion can befulfilled.
 17. The system of claim 12, wherein identifying at least oneof an alternate item, an alternate fulfillment center, or an alternatetime to include in the alternate suggestion based on the storedhistorical user information that indicates a likelihood that the userwill accept the alternate suggestion comprises: determining that theuser is likely to be interested in the alternate item based on thestored historical user information that indicates a likelihood that theuser will accept the alternate suggestion; and in response todetermining that the user is likely to be interested in the alternateitem based on the stored historical user information that indicates alikelihood that the user will accept the alternate suggestion,identifying the alternate item.
 18. The system of claim 12, whereinidentifying at least one of an alternate item, an alternate fulfillmentcenter, or an alternate time to include in the alternate suggestionbased on the stored historical user information that indicates alikelihood that the user will accept the alternate suggestion comprises:determining a likely route for the user based at least on the storedhistorical user information that indicates a likelihood that the userwill accept the alternate suggestion; determining that the alternatefulfillment center is accessible along the likely route; and in responseto determining that the alternate fulfillment center is accessible alongthe likely route, identifying the alternate fulfillment center.
 19. Anon-transitory computer-readable medium storing software comprisinginstructions executable by one or more computers which, upon suchexecution, cause the one or more computers to perform operationscomprising: tracking, over time and by bulk order processing server ofan order processing system that includes (i) the bulk order processingserver, (ii) local fulfillment center workstations that are eachassociated with a different third party fulfillment center, and (iii)multiple user devices, performance of the fulfillment center infulfilling orders for items at the fulfillment center based on datacollected at the local fulfillment center workstations, and userresponses to alternate suggestions based on data collected at themultiple user devices; based on the tracking by the bulk orderprocessing server, storing, in an electronic database accessible to thebulk order processing server of the order processing system, historicalpreparation information that describes historical performance of thefulfillment center in fulfilling orders for items at the fulfillmentcenter based on the data collected at the local fulfillment centerworkstations; based on the tracking by the bulk order processing server,storing, in the electronic database accessible to the bulk orderprocessing server of the order processing system, historical userinformation that indicates a likelihood that a user indicate acceptanceof an alternate suggestion at one of the user devices; receiving, over anetwork, by the bulk order processing server, and from one of the userassociated with the user and the order processing system, an order foran item to be fulfilled by a particular local fulfillment center for afulfillment time, where the order indicates the item and the particularfulfillment center fulfills the order by enabling an employee at thefulfillment center to prepare the item for the user, wherein theparticular user device is a mobile computing device and the order isentered through a graphical user interface of a web applicationpresented on the mobile computing device; in response to receiving theorder from the user device: obtaining, from the database accessible tothe bulk order processing server and based at least on the indication inthe order of the fulfillment center, a portion of the stored historicalpreparation information for the fulfillment center that describeshistorical performance of the fulfillment center that is relevant tofulfillment of the item by the fulfillment time; obtaining, based atleast on the indication in the order of the fulfillment center, currentorder information that describes other orders to be fulfilled at thefulfillment center; and determining, by the bulk order processingserver, to provide an alternate suggestion for the user device toprovide to the user through the graphical user interface of the webapplication based at least on a portion of the stored historicalpreparation information and the current order information; in responseto determining to provide the alternate suggestion for the user deviceto provide to the user through the graphical user interface of the webapplication, identifying, by the bulk order processing server, at leastone of an alternate item, an alternate fulfillment center, or analternate time to include in the alternate suggestion based on thestored historical user information that indicates a likelihood that theuser will indicate acceptance of an alternate suggestion at one of theuser devices and further based at least on historical performance of thealternate fulfillment center, current orders for the alternatefulfillment center, historical performance for the alternate item,current orders for the alternate item, or current orders for thealternate time; generating, by the bulk order processing server, thealternate suggestion based on the identification of at least one of thealternate item, the alternate fulfillment center, or the alternate timeto include in the alternate suggestion; and providing, over the networkand by the bulk order processing server, instructions that trigger thegraphical user interface of the web application on the user device to(i) provide the alternate suggestion to the user without the userproviding further input after the user enters the order through thegraphical user interface of the web application and (ii) disableentering, in the graphical user interface of the web application, arequest for the item from the fulfillment center.